Constructing dynamic model for thermal flow forecasting based on experimental data

We consider an identification algorithm for a dynamic object. The algorithm allows for choosing any rows from the block of the transformed empirical input-output data. Moreover, the algorithm enables passing to a parameter space and involving a priori information on the domain of parameter existence to improve accuracy. These features serve to construct a model of the dynamic object with a smaller forecasting error. The algorithm is applied for mathematical modeling of a fuel assembly in a nuclear reactor based on experimental heat transfer data.